ai-startup-building_skill

Overview

This skill builds AI-native products using Dan Shipper’s 5-product playbook and Brandon Chu’s AI product frameworks, updated for 2025+ best practices. It guides product teams through prompt engineering, AI-native UX, cost optimization, and scalable model architectures. The focus is on rapid shipping, data-driven iteration, and efficient distribution so small teams can achieve outsized outcomes. Use it to turn AI capabilities into repeatable product playbooks and measurable business results.

How this skill works

The skill inspects product requirements and converts them into actionable AI patterns: streaming responses, structured JSON outputs, model routing, caching, batching, and retry logic. It provides implementation templates and cost-analysis blueprints to quantify savings from caching and model selection. It also prescribes metrics and checkpoints for latency, error rate, and per-user cost so you can iterate on both product and infrastructure.

When to use it

• Designing an AI-first feature that needs low latency and reliable outputs
• Implementing prompt engineering with structured outputs and evals
• Planning cost optimization across models, caching, and batching
• Scaling an AI product while keeping error rates and costs in check
• Creating an AI-native UX that streams and updates in real time

Best practices

• Return structured outputs (JSON) to make downstream logic deterministic
• Implement streaming + retry logic for responsiveness and reliability
• Route simple queries to smaller models and complex ones to larger models
• Cache aggressively—target an 80% hit rate for repeat queries
• Batch non-real-time requests to reduce per-request overhead
• Design model swappability and monitor token usage continuously

Example use cases

• A writing assistant that streams content as it’s generated and caches templates for repeat prompts
• A customer-support agent that routes short FAQs to a small model and escalates complex tickets to a larger model
• A product that batches nightly data enrichment jobs to cut per-call costs
• Rapidly validating multiple AI product ideas by launching incremental MVPs and measuring real usage
• A pricing and cost dashboard that projects savings after applying caching and model routing